Data was avaiable in a .csv format. In the original data file, the one column was of the Country, one was of Type and the rest of the columns were the years from 1960-2020. However, we changed the columns and rows so that it can be used easily. Therefore, in rows there was one column of Country, one column of Year and the rest 16 column for different kind of catgoeries required for conclusion. Each cell would give us the value corresponding to the particular category for a given year and country. However, the data was not available for all years and categories for every country. Therefore, we did not include the years from 1960-2000. For Expenditure we had a separate sheet, in that we took years from 2000-2020 and we included only those countries whose data was available for atleast 15 years. In the remaining expenditure sheet, the values were filled using the mean value of all years.
#2000-2020
#x-axis -> %male
#y-axis -> %female
#size -> max(total teachers)
#Animation -> year
from plotly.offline import plot, iplot, init_notebook_mode
init_notebook_mode(connected=True)
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import plotly.express as px
#from google.colab import files
import plotly.graph_objects as go
df = pd.read_csv('./data/Data_final.csv')
mean_value=df['Primary Trained Male'].mean()
df['Primary Trained Male'].fillna(value=mean_value, inplace=True)
mean_value=df['Primary Trained Female'].mean()
df['Primary Trained Female'].fillna(value=mean_value, inplace=True)
x = {*()}
for i in range(16226):
y = df.iloc[i]['Country Name']
x.add(y)
country_name = list(x)
year = []
for i in range(2000,2021):
year.append(i)
for y in range(2000,2020):
x_arr = []
y_arr = []
ratio = []
for i in range(16226):
if(df.iloc[i]['Year'] == y):
x_arr.append(df.iloc[i]['Primary Trained Male'])
y_arr.append(df.iloc[i]['Primary Trained Female'])
ratio.append(df.iloc[i]['Primary Trained Female']/df.iloc[i]['Primary Trained Male'])
fig = px.scatter(df, x=x_arr, y=y_arr,size=ratio, hover_data=[country_name], color = country_name, title = "Female Trained Teachers / Male Trained Teachers in different countries"+str(" ")+str( y), labels=dict(x="Primary Trained Male (%)", y="Primary Trained Female (%)", color="Place"))
fig.show()
# print(ratio)
# new_ratio = np.array([x*2000 for x in ratio])
The above graph is plotted year wise between percentage of Primary Trained Male teachers and percentage of Primary Trained Female teachers. The different colored circles displays different countries. However, since for few countries we do not have many data therefore, we have some of the bubbles as outliers for which we cannot bring out any conclusion.
Overall in the graph we can see that from year 2000 to year 2021 both the percentage of Trained Male and Trained Female teachers have increased. Many countries during the year 2000 had number of Trained Male and Trained Female Teacher nearly less than 50 percent. But By 2021 majority countries reached nearly to the hundred percent of trained Male and Female primary teachers.
Bahrain recently 2020 reported having 100% Trained Teachers for primary education. This data remained consistent with the previous year's numbers, i.e., 2019. In Bahrain, the average Trained teacher from 2011 to 2020 is around 83%. In 2011 Bahrain reported that around 80% of the trained teachers in both the Male and Female categories. From 2011 to 2017, the ratio of trained teachers in primary education increased slowly. Whereas, from 2017 to 2018, it was a sudden increase. The data shows that the percentage of trained female teachers in Bahrain is slightly more significant than the percentage of trained male teachers. There is no gender discrimination in Bahrain as per their constitution formed in 2002. The result of having more trained female teachers than males shows the openness of the country Bahrain. Another reason for the increased trained teacher can be due to the internet. With the help of the internet, more and more teachers in Bahrain can become trained professionals.
Caribbean countries are a few countries that lag in terms of primary education. Their number of Trained teachers is not satisfying. However, the number of Trained teachers in females has a significantly higher percentage than Male trained teachers and this is true after the fact that most of the Teachers in Caribbean countries are Female. Over the years, Trained female teachers have gone from 77% to around 88% whereas the number of trained male teachers increased by only 4 percent. This is seen because of low-level remuneration, many of the trained teachers leave the profession to take up more lucrative posts in the private sector or migrate to countries like the Bahamas.
Ghana in particular has seen a relative decrease in the percentage trained teachers in both males and females. More particularly, in females the percentage decreased from 88% to 77%. The explanation for this is that the percentage of female teachers gradually increased relatively. As more instructors arrived, it became increasingly difficult to train them. Also, Ghana is a country suffering from gender discrimination, females are not allowed many freedoms. Therefore, females are kept untrained. However, the percentage of trained male teachers initially decreased because more and more males were becoming teachers; it was challenging for them to be trained. However, in the end percentage of trained male teachers will remain the same in 2020 as in 2000.
# Net Enrollment Rate
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px
iris_df = pd.read_csv("./data/Data_final (1).csv", index_col = "Year")
# mean_value = iris_df['Net Enrollment Male'].mean()
# iris_df['Net Enrollment Male'].fillna(value=mean_value, inplace=True)
# mean_value = iris_df['Net Enrollment Female'].mean()
# iris_df['Net Enrollment Female'].fillna(value=mean_value, inplace=True)
# mean_value = iris_df['Net Enrollment'].mean()
# iris_df['Net Enrollment'].fillna(value=mean_value, inplace=True)
arr = [i for i in range(1960,2000)]
iris_df.drop(arr, axis=0, inplace=True)
r = ["Net Enrollment","Net Enrollment Female","Net Enrollment Male"]
iris_df = iris_df.dropna(subset = r)
# print(iris_df.shape)
# ratio = []
# for i in range(2138):
# ratio.append(iris_df.iloc[i]['Net Enrollment Female']/iris_df.iloc[i]['Net Enrollment Male'])
# # print(ratio)
# iris_df['ratio'] = ratio
# print(iris_df.ratio)
# r = ["Net Enrollment","Net Enrollment Female","Net Enrollment Male"]
# iris_df = iris_df.dropna(subset = r)
# print(iris_df.head)
iris_df['Year1'] = iris_df.index
# df['Year1']
# print(iris_df.Year1)
# display(iris_df.Year1.to_string())
# iris_df['Net Enrollment'] = iris_df['Net Enrollment']/1000
# female_pow = []
# for i in range(2138):
# female_pow.append( (iris_df.iloc[i]['Net Enrollment Female'])**3 )
# iris_df['female_pow'] = female_pow
iris_df = iris_df.sort_values('Year1', ascending=True)
fig = px.scatter_geo(iris_df, locations="Country Code", size="Net Enrollment Female", size_max=10, animation_group = "Country Name",
projection="natural earth",color="Country Name", animation_frame="Year1", template="plotly_dark")
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
This shows percentage of the female enrollment across various countries. The size of the circles shows the relative percentage of females. If the size is larger, it signifies that the percentage of female enrolment in that nation is higher than in other countries. If the size is more significant, that means that in that particular country percentage of the female enrollment is greater than that of other countries. In the Western Countries we can see that the relative percentage of female enrollment in primary education is lower than that of most other countries. This is due to West Africa having different cultures and having gender discrimination. People in Western Africa do not feel it appropriate to educate women. However, we can see that the percentage of female enrollment has increased in those countries over the past few years. This is due to more awareness of gender equality and women empowerment. We can also see that India had an initial percentage of female enrollment of around 70 during 2000. However, by 2012 women empowerment and gender equality turned the percentage to near 90%. Similarly, our neighboring country Bhutan had 48% female enrollment. However, in 2018 percentage of female enrollment reached 90%.
This data shows how the awareness of gender equality has changed the world. It clearly shows how the percentage female enrollment in primary education has increased. These changes are due to modernization. Hence, women's empowerment has improved the ratio of females in education. As we know, education is most important. Therefore, educating more females can change the world regarding women's empowerment.
#pie
# Trained teacher vs Total teacher
# Primary Trained vs Primary Education Teacher (number)
import plotly.express as px
import matplotlib.pyplot as plt
import numpy as np
#from google.colab import files
df = pd.read_csv("./data/Data_final (1).csv")
# print(df.head())
arr = [i for i in range(1960,2000)]
df.drop(arr, axis=0, inplace=True)
r = ["Primary Trained","Primary Education Teacher"]
df = df.dropna(subset = r)
# print(df.shape)
Train_no = []
for i in range(1047):
Train_no.append(( (df.iloc[i]["Primary Trained"])*(df.iloc[i]["Primary Education Teacher"]) )/100)
df['Train_no'] = Train_no
sum_train = []
for y in range(2000,2021):
sum_a = []
for i in range(1047):
if(df.iloc[i]['Year'] == y):
sum_a.append(df.iloc[i]['Train_no'])
sum_train.append(sum(sum_a))
sum_total = []
for y in range(2000,2021):
sum_a = []
for i in range(1047):
if(df.iloc[i]['Year'] == y):
sum_a.append(df.iloc[i]['Primary Education Teacher'])
sum_total.append(sum(sum_a))
# print(sum_train)
# print(sum_total)
pie_percent = []
for i in range(21):
pie_percent.append((sum_train[i]/sum_total[i])*100)
#change values of i in pie_percent[i] and 100-pie_percent[i] to get different pie graphs of repective year
for i in range(10,18):
y = np.array( [pie_percent[i],100-pie_percent[i]])
mylabels = ["Trained", "UnTrained"]
explode = (0.1, 0)
plt.pie(y, labels = mylabels,autopct='%1.1f%%', explode = explode, shadow=True)
plt.title("World Trained Teacher vs World Non-Trained Teacher"+str(" ")+ str(i+2000))
plt.savefig(str(i+2000)+".png")
plt.show()
Each pie chart shows the year wise percentage value of trained and untrained primary teachers all over the world. For calculation of this part we first added all the primary teacher year wise, and then added all the untrained teacher year wise. Then, we calculated the percentage of untrained primary teachers. The remaining percentage is conveying the percentage of trained teachers.
In 2000 total untrained teachers all over the world were about 17%. However, in 2011 the number of untrained teachers was 27%. The vast majority of the trained teachers were changing professions around those times. The teaching profession was a low self-esteem job. Therefore, all the trained professionals were leaving their jobs and were applying for higher esteem jobs with higher salaries. This gradually increased the percentage of untrained teachers, as did the addition of more teachers worldwide. This gradually increased the percentage of untrained teachers, as did the addition of more teachers worldwide. In 2011, more people became aware of primary education for a country. Therefore, more and more teachers were being trained and people were again interested in the teaching profession. Recognizing the significance of elementary education, every country strives to recruit more teachers by offering competitive wages and preparing them through competitive tests.. Therefore, the pie chart shows a gradual decrease in the percentage of untrained teachers. Also, we see a 7% decrease in the percentage of untrained teachers from the year 2019 to 2020. The reason being, due to covid we shifted from offline mode to online mode. The online mode of education made it easier to train teachers, increasing the number of trained teachers and decreasing the percentage of untrained teachers. Overall, from 2000 to 2020 we see a net decrease in untrained teachers. Therefore, this gives a positive impression about the overall world moving toward modernization and education.
import pandas as pd
import bar_chart_race as bcr
# import pandas as pd
# from matplotlib import pyplot as plt
# # Read CSV into pandas
# data = pd.read_csv(r"expen_final.csv")
# data.head()
# df = pd.DataFrame(data)
# name = df['car'].head(12)
# price = df['price'].head(12)
# # Figure Size
# fig = plt.figure(figsize =(10, 7))
# # Horizontal Bar Plot
# plt.bar(name[0:10], price[0:10])
# # Show Plot
# plt.show()
df=pd.read_csv("./data/expen_final.csv", index_col = "Year")
# df.head()
# df.isnull().sum(axis =0)
#perc = 75.0
#min_count = int(((100-perc)/100)*df.shape[0] + 1)
#df = df.dropna( axis=1,
# thresh=min_count)
ls = list(df)
g = len(ls)
for i in range(g):
var = df[ls[i]].isna().sum()
#var = df[ls[i]]
if(var > 6):
#df.drop(ls[i], axis =1)
del df[ls[i]]
l = list(df)
x=len(l)
for i in range(1,x):
m = df[l[i]].mean()
df[l[i]].fillna(value = m, inplace = True)
bcr.bar_chart_race(df=df, sort='desc', steps_per_period=3, title="Expenditure of countires in different years",figsize=(5, 9), n_bars = 60)
From the plots we can see that the expenditure on primary education out of total expenditure on education is more for under-developed countries. Whereas, the developed countries have less expenditure on primary education out of total expenditure on education. For e.g. Finland is a country with the highest literacy rate, it spends less on primary education than higher education. Whereas, the region of West Africa spends more than 50% of its total expenditure on primary education. Because, under-developed and developing countries require more focus on primary education since it is an essential investment in developing a country.
Similarly, for South Africa we can see that the percentage spent on primary education is more significant from 2000 to 2012. Since then, several countries in South Africa have grown to the point where the emphasis has shifted from primary education to higher education.
OVERALL CONCLUSION
The overall conclusion is that the importance of primary education is taken seriously by every country. Primary education is an essential investment for all developing countries and developed countries.